A vision transformer approach for fully automated and scalable dementia screening using clock drawing test images.

ABSTRACT:

Introduction

The clock drawing test (CDT) screens for dementia but requires trained scorers and lacks standardized criteria. Thus, we developed an automated vision transformer (ViT)-based diagnostic system with convolutional neural network preprocessing for analyzing hand-drawn CDT images.

Methods

The architecture implements fine-tuned ViT feature extraction with linear classification for dementia prediction. Training used the National Health and Aging Trends Study (NHATS) dataset (n = 54,027), with testing on an independent clinical cohort from the Toronto Dementia Research Alliance (TDRA; n = 862; 522 dementia, 340 normal cognition).

Results

The ViT approach predicted dementia with 76.5% balanced accuracy, outperforming human-scored features (74.3%) an

SUBMITTER: Bone MB 

PROVIDER: S-EPMC12457074 | biostudies-literature | 2025 Jul-Sep

REPOSITORIES: biostudies-literature